Many statistical problems involve the estimation of a (d× d) orthogonal matrix Q. Such an estimation is often challenging due to the orthonormality constraints on Q. To cope with this problem, we use the well-known PLU decomposition, which factorizes any invertible (d× d) matrix as the product of a (d× d) permutation matrix P, a (d× d) unit lower triangular matrix L, and a (d× d) upper triangular matrix U. Thanks to the QR decomposition, we find the formulation of U when the PLU decomposition is applied to Q. We call the result as PLR decomposition; it produces a one-to-one correspondence between Q and the d(d- 1) / 2 entries below the diagonal of L, which are advantageously unconstrained real values. Thus, once the decomposition is applied, regardless of the objective function under consideration, we can use any classical unconstrained optimization method to find the minimum (or maximum) of the objective function with respect to L. For illustrative purposes, we apply the PLR decomposition in common principle components analysis (CPCA) for the maximum likelihood estimation of the common orthogonal matrix when a multivariate leptokurtic-normal distribution is assumed in each group. Compared to the commonly used normal distribution, the leptokurtic-normal has an additional parameter governing the excess kurtosis; this makes the estimation of Q in CPCA more robust against mild outliers. The usefulness of the PLR decomposition in leptokurtic-normal CPCA is illustrated by two biometric data analyses.

### Unconstrained representation of orthogonal matrices with application to common principal components

#### Abstract

Many statistical problems involve the estimation of a (d× d) orthogonal matrix Q. Such an estimation is often challenging due to the orthonormality constraints on Q. To cope with this problem, we use the well-known PLU decomposition, which factorizes any invertible (d× d) matrix as the product of a (d× d) permutation matrix P, a (d× d) unit lower triangular matrix L, and a (d× d) upper triangular matrix U. Thanks to the QR decomposition, we find the formulation of U when the PLU decomposition is applied to Q. We call the result as PLR decomposition; it produces a one-to-one correspondence between Q and the d(d- 1) / 2 entries below the diagonal of L, which are advantageously unconstrained real values. Thus, once the decomposition is applied, regardless of the objective function under consideration, we can use any classical unconstrained optimization method to find the minimum (or maximum) of the objective function with respect to L. For illustrative purposes, we apply the PLR decomposition in common principle components analysis (CPCA) for the maximum likelihood estimation of the common orthogonal matrix when a multivariate leptokurtic-normal distribution is assumed in each group. Compared to the commonly used normal distribution, the leptokurtic-normal has an additional parameter governing the excess kurtosis; this makes the estimation of Q in CPCA more robust against mild outliers. The usefulness of the PLR decomposition in leptokurtic-normal CPCA is illustrated by two biometric data analyses.
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2021
Common principal components
FG algorithm
Leptokurtic-normal distribution
LU decomposition
Orthogonal matrix
QR decomposition
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Utilizza questo identificativo per citare o creare un link a questo documento: `https://hdl.handle.net/20.500.11769/523710`
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